Inverse design and flexible parameterization of meta-optics using algorithmic differentiation

TL;DR

This paper introduces a flexible, efficient method for designing advanced meta-optics using algorithmic differentiation, enabling rapid, multifunctional optical device development beyond traditional techniques.

Contribution

It applies algorithmic differentiation to photonic design, supporting arbitrary scatterers and topology optimization, and provides an open-source platform for fast meta-optical design.

Findings

Supports complex, multilayer meta-optics with rapid iteration

Enables arbitrary parameterization and topology optimization

Offers an open-source, adaptable design platform

Abstract

Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the adjoint method, this requires explicitly deriving gradients, which is sometimes challenging for certain photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each focused in scope and scatterer type. Here, we leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the cost of two forward simulations typical of adjoint methods, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design.